South Africa: Data Scientist AI/ML

About the Role

The company assists clients in becoming data-driven by applying data technology to create real-time, actionable visibility into business performance and lasting analytics capability. This role will help expand delivery capacity for pragmatic, production-grade analytics, ML, and GenAI solutions, enabling client teams to move from “interesting insights” to deployed capabilities with measurable value.

Key Responsibilities

• Translate client business questions into analytic approaches (metrics, segments, hypotheses)
• Perform data exploration to identify drivers, anomalies, and performance constraints
• Communicate findings clearly to non-technical stakeholders
• Design, run, and analyze experiments (A/B tests) and/or apply causal inference where experimentation is constrained
• Build predictive models with sensible baselines, validation, and error analysis, document tradeoffs
• Build LLM/GenAI applications (prompt workflows, tool use, structured outputs)
• Implement guardrails: PII handling, secure prompting patterns, input/output validation, rate limiting, and fallback behaviors
• Package models and GenAI components into clean services (APIs/batch jobs), with basic CI/CD awareness and production readiness
• Improve/extend data pipelines and feature reliability (freshness, data quality checks, lineage-friendly design)
• Contribute to value measurement: define success metrics, track outcomes, and help clients understand what moved and why

Requirements

• Strong fundamentals in SQL and Python for analysis, transformation, and reproducible work
• Experience delivering at least one end-to-end analytics or ML project (problem framing data model/analysis decision or deployment)
• Working knowledge of experimentation (A/B testing) and common pitfalls; comfort explaining statistical reasoning simply
• Exposure to LLM application development (prompting, a small RAG prototype, or integrating an LLM into a workflow)
• ML engineering fundamentals: Git, code reviews, basic testing mindset, and ability to build/maintain a simple API or batch pipeline
• Data quality mindset: proactively validating inputs/outputs and implementing basic checks (freshness, nulls, ranges, duplicates)

Qualifications

• Formal education/certifications in relevant fields (Bachelor’s, LLB, Matric, etc.)

Salary & Benefits

• Salary range to be discussed

Note: The original job description did not mention salary or benefits.